Whose name is the most, least or rarest in the family?¶

Image

IMPORTS¶

In [1]:
import pandas as pd
import matplotlib.pyplot as plt
from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource
from bokeh.models import NumeralTickFormatter
from bokeh.io import output_notebook
import seaborn as sns
import plotly.express as px
%matplotlib inline
output_notebook()
Loading BokehJS ...
In [2]:
#Setup family
females = ["Sarah"]
males= ["Jason","Leon","Conrad"]
In [3]:
#Create some lists to select or reformat data later
count_cols = []
rank_cols = []
years = []
for f in range(1996,2022):
    count_cols.append(str(f) + ' Count')
    rank_cols.append(str(f) + ' Rank')
    years.append(str(f))
#years = list(years)
In [4]:
#Import ONS data
df1 = pd.read_excel("babynames1996to2021.xlsx","1",skiprows =7)
df2 = pd.read_excel( 'babynames1996to2021.xlsx',"2",skiprows =7)
df1 = df1.set_index("Name")
df2 = df2.set_index("Name")
In [5]:
#Replace NaN with 0
df1 = df1.replace("[x]",0)
df2 = df2.replace("[x]",0)
In [6]:
#Filter names from ONS data
Girls = df2.loc[females]
Boys = df1.loc[males]
df_family = pd.concat([Girls, Boys], axis=0)
In [7]:
df_family_counts = df_family.drop(columns=rank_cols)
df_family_ranks = df_family.drop(columns=count_cols)
In [8]:
df_family_counts.columns = df_family_counts.columns.str.replace(" Count","")
df_family_ranks.columns = df_family_ranks.columns.str.replace(" Rank","")
In [9]:
df_family_counts = pd.pivot_table(df_family_counts, values = years, columns=["Name"])
df_family_counts.index.name = "Year"
df_family_ranks = pd.pivot_table(df_family_ranks, values = years, columns=["Name"])
df_family_ranks.index.name = "Year"
In [10]:
# create a new plot with a title and axis labels
source = ColumnDataSource(df_family_counts)
p = figure(title="Name rankings", x_axis_label="Year", y_axis_label="Babies Named",x_range=years, width=900, height=480)

# add a line renderer with legend and line thickness
p.line(x = "Year", y = "Jason", legend_label="Jason", line_width=2,source=source)
p.line(x = "Year", y = "Sarah", legend_label="Sarah", line_color="red",line_width=2,source=source)
p.line(x = "Year", y = "Leon", legend_label="Leon", line_color="yellow",line_width=2,source=source)
p.line(x = "Year", y = "Conrad", legend_label="Conrad", line_color="darkgrey",line_width=2,source=source)

# show the results
show(p)
In [11]:
# create a new plot with a title and axis labels
source = ColumnDataSource(df_family_ranks)
p = figure(title="Name rarity", x_axis_label="Year", y_axis_label="Name UK rank",x_range=years, width=900, height=480)

# add a line renderer with legend and line thickness
p.line(x = "Year", y = "Jason", legend_label="Jason", line_width=2,source=source)
p.line(x = "Year", y = "Sarah", legend_label="Sarah", line_color="red",line_width=2,source=source)
p.line(x = "Year", y = "Leon", legend_label="Leon", line_color="yellow",line_width=2,source=source)
p.line(x = "Year", y = "Conrad", legend_label="Conrad", line_color="darkgrey",line_width=2,source=source)

# show the results
show(p)
In [12]:
df_family_ranks.tail(12)
Out[12]:
Name Conrad Jason Leon Sarah
Year
2010 536 161 60 83
2011 576 159 62 90
2012 734 165 72 95
2013 611 155 75 96
2014 714 162 85 95
2015 779 163 85 96
2016 870 170 92 96
2017 861 179 98 103
2018 851 174 97 103
2019 1152 175 93 107
2020 921 213 93 115
2021 1448 210 101 125
In [13]:
df_family_counts.tail(12)
Out[13]:
Name Conrad Jason Leon Sarah
Year
2010 64 344 1204 722
2011 61 360 1069 663
2012 45 359 918 592
2013 56 372 837 574
2014 45 353 765 601
2015 41 355 795 581
2016 36 340 737 572
2017 36 314 669 530
2018 36 311 655 503
2019 23 302 662 479
2020 31 240 620 422
2021 17 251 590 403
In [14]:
df_family_counts.plot(title="Name rankings",figsize=(11,6),grid=True)
Out[14]:
<AxesSubplot:title={'center':'Name rankings'}, xlabel='Year'>
In [15]:
fig, ax = plt.subplots(figsize=(15, 8))
sns.lineplot(df_family_counts).set(title="Name rankings")
Out[15]:
[Text(0.5, 1.0, 'Name rankings')]
In [16]:
df1_totals = df1.drop(columns=rank_cols).sum()
df2_totals = df2.drop(columns=rank_cols).sum()
df1_totals = df1_totals.reset_index()
df2_totals = df2_totals.reset_index()
df_totals = pd.concat([df1_totals, df2_totals], axis=0)
df_totals = df_totals.replace(count_cols,years)
#df_totals = df1_totals + df2_totals
df_totals.columns=["Years","Total"]
df_totals = df_totals.groupby("Years").sum()

df_totals.plot(title="Total Births",figsize=(11,6),grid=True)
Out[16]:
<AxesSubplot:title={'center':'Total Births'}, xlabel='Years'>
In [17]:
px.line(df_totals)
In [18]:
df_totals
Out[18]:
Total
Years
1996 614515
1997 607636
1998 599650
1999 584935
2000 566359
2001 555831
2002 555680
2003 578230
2004 594375
2005 597737
2006 619598
2007 637496
2008 655171
2009 651553
2010 667340
2011 667230
2012 670522
2013 642085
2014 638852
2015 641216
2016 639126
2017 621991
2018 600913
2019 583969
2020 557458
2021 569103
In [19]:
px.line(df_family_counts)